Dairy production is identified as a major source of methane (CH4) emissions. Selective breeding canbe one method used to mitigate CH4 emissions but practical and cheap measurement of this trait is not ... [more ▼]

Dairy production is identified as a major source of methane (CH4) emissions. Selective breeding canbe one method used to mitigate CH4 emissions but practical and cheap measurement of this trait is not currently available. The mid-infrared (MIR) prediction of milk fatty acids is relevant in this context. Five MIR methane indicators were derived from the literature and were calibrated from 600 samples analyzed by gas chromatography. Genetic parameters for these traits were estimated using single trait random regression test-day models from 619,265 records collected from 2007 to 2011 on 71,188 Holstein cows in their first three lactations. For the published indicator showing the highest relationship with the methane data (R2 = 0.88), the average daily heritability was 0.34±0.01, 0.37±0.01 and 0.34±0.01 for the first three lactations, respectively. The methane emission (g/day) was increased from beginning of lactation, reached at the highest in peak of lactation and decreased towards end of lactation. The largest differences between estimated breeding values (EBV) of sires having daughters in production eructing the highest and the lowest methane content was 21.80, 22.75 and 24.89 kg per lactation for the first three parities. Positive genetic correlations were estimated between indicator traits and milk fat and protein content. Low negative correlation was observed with milk yield. In conclusion, this study shows the feasibility to predict methane indicator traits by MIR. Moreover, the estimated genetic parameters suggest also a potential genetic variability of the quantity of methane eructed by dairy cows. [less ▲]

Body weight (BW) of dairy cows can be estimated using linear conformation traits (calculated BW; CBW), which are generally recorded only once during a lactation. However, predicted BW (PBW) throughout the ... [more ▼]

Body weight (BW) of dairy cows can be estimated using linear conformation traits (calculated BW; CBW), which are generally recorded only once during a lactation. However, predicted BW (PBW) throughout the lactation would be useful, e.g., at milk-recording dates allowing feed-intake prediction for advisory purposes. Therefore, a 2-step approach was developed to obtain PBW for each milk-recording date. In the first step, a random-regression test-day model was used with CBW as observations to predict PBW. The second step consisted in changing means and (co)variances of prior distributions for the additive genetic random effects of the test-day model by using priors derived from results of the first step to predict again PBW. A total of 25,061 CBW from 24,919 primiparous Holstein cows were computed using equations from literature. Using CBW as observations, PBW was then predicted over the whole lactation for 232,436 dates corresponding to 207,375 milk-recording dates and 25,061 classification dates. Results showed that using both steps (the 2-step approach) provided more accurate predictions than using only the first step (the one-step approach). Based on the results of this preliminary study, BW of dairy cows could be predicted throughout the lactation using this procedure. These predictions could be useful in milk-recording systems to compute traits of interest (e.g., feed-intake prediction). The developed novel method is also flexible because actual direct measurements of BW can also be used together with CBW, the prediction model being able to accommodate different levels of accuracies of used BW phenotypes. [less ▲]

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a dataset including 33,155 calving records. Included in the models were season, herd and sex of calf age of dam classes group of calvings interaction as fixed effects, herd year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was about 8% with linear models and about 12% with threshold models. Maternal heritabilities were about 2% and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17% and 23 % greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice. [less ▲]

High-perfomance computing facilities proposing shared-memory and distributed-memory multiprocessors are becoming available. With those clusters, parallel computing could lead to increased performances and ... [more ▼]

High-perfomance computing facilities proposing shared-memory and distributed-memory multiprocessors are becoming available. With those clusters, parallel computing could lead to increased performances and problem sizes. However, to our knowledge and especially for variance components estimations, most software available in animal breeding, based on sparse matrices computations, do not allow parallel computing and are limited by memory accessible by the central processing unit, or allow parallel computing only for options with dense matrices computations, which limits anyway problem sizes due to storage of dense matrices. The aim was to propose simple and effective modifications for the BLUPF90 family of programs to reduce computing time with consideration of required memory. Modifications were based on academic free packages proposing solver and sparse inversion for sparse symmetric indefinite linear systems. First, modifications concerned the sparse inversion subroutine implemented in the package FSPAK. Rearrangements of 'do' loops to allow optimizations of computer operations by some compilers and addition of OpenMP directives were performed. The ordering operation was modified to more easily compare a multiple minimum degree algorithm (MMD; implemented in FSPAK) and a multilevel nested dissection algorithm (implemented in METIS 4.0.3). Second, the package PARDISO Version 5.0.0 was used instead of FSPAK. This package proposes in particular a parallel solver and sparse inversion on shared-memory multiprocessors. Modified FSPAK and PARDISO were compared to original FSPAK using MMD through REMLF90. Different models, such as univariate or bivariate (random regressions) test-day animal and single-step genomic models, were tested. All jobs were run 5 times. With an appropriate ordering algorithm, speedup for each REMLF90 iteration were up to 7.5 for modified FSPAK and up to 22.8 for PARDISO with 2 threads. With 4 threads, speedup increased to 8.3 and 32.5, respectively. [less ▲]

in Book of Abstracts of the 65th Annual Meeting of the European Federation of Animal Science (2014, August 28)

High-perfomance computing facilities proposing shared-memory and distributed-memory multiprocessors are becoming available. With those clusters, parallel computing could lead to increased performances and ... [more ▼]

High-perfomance computing facilities proposing shared-memory and distributed-memory multiprocessors are becoming available. With those clusters, parallel computing could lead to increased performances and problem sizes. However, to our knowledge and especially for variance components estimations, most software available in animal breeding, based on sparse matrices computations, do not allow parallel computing and are limited by memory accessible by the central processing unit, or allow parallel computing only for options with dense matrices computations, which limits anyway problem sizes due to storage of dense matrices. The aim was to propose simple and effective modifications for the BLUPF90 family of programs to reduce computing time with consideration of required memory. Modifications were based on academic free packages proposing solver and sparse inversion for sparse symmetric indefinite linear systems. First, modifications concerned the sparse inversion subroutine implemented in the package FSPAK. Rearrangements of 'do' loops to allow optimizations of computer operations by some compilers and addition of OpenMP directives were performed. The ordering operation was modified to more easily compare a multiple minimum degree algorithm (MMD; implemented in FSPAK) and a multilevel nested dissection algorithm (implemented in METIS 4.0.3). Second, the package PARDISO Version 5.0.0 was used instead of FSPAK. This package proposes in particular a parallel solver and sparse inversion on shared-memory multiprocessors. Modified FSPAK and PARDISO were compared to original FSPAK using MMD through REMLF90. Different models, such as univariate or bivariate (random regressions) test-day animal and single-step genomic models, were tested. All jobs were run 5 times. With an appropriate ordering algorithm, speedup for each REMLF90 iteration were up to 7.5 for modified FSPAK and up to 22.8 for PARDISO with 2 threads. With 4 threads, speedup increased to 8.3 and 32.5, respectively. [less ▲]

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as ... [more ▼]

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as methane emission (CH4), dry matter intake (DMI) and feed efficiency (FE); however they are available on a small scale. Researches showed that CH4 could be predicted from milk mid-infrared (MIR) spectra, allowing large-scale recording at low cost. The main objective of this study was to show, using a modelling approach, that DMI and FE could be derived from milk MIR spectra. For that, knowledge of body weight (BW) is required; however it was unknown in this study. Derived procedure was based on milk yield and composition, MIR CH4, and modelled standard animal requirements, allowing the prediction of expected BW. An external validation was conducted based on 91 actual records. 95% confidence limit for the difference ranged between -0.66 and 18.84 kg for BW, from -0.02 to 0.26 kg/day for DMI, and from -0.02 to 0.002 kg of fat corrected milk/kg DM for FE. Root mean square errors were 39.66 kg, 0.56 kg/d, and 0.03 kg/DM for the 3 studied traits. P-value for the t-test was not significant for BW and DMI. This suggests the possibility to obtain expected BW and therefore DMI from MIR spectra. Single trait animal test-day models used 1,291,850 records to assess the variability of studied traits. Significant variations were observed for the lactation stage, parity, genetics, and age. These findings were in agreement with the literature except for early lactation. This suggests in conclusion that the MIR information gave similar results for DMI and CH4 for the major part of lactation. The use of this novel method to predict expected BW offers new possibilities interesting for the development of genomic and genetic tools. [less ▲]

in Proceedings of the 10th World Congress on Genetics Applied to Livestock Production (2014, August)

Improvement of dairy cow fertility by means of genetic selection has become increasingly important over the last decades. Because fertility traits are difficult to measure and have low heritabilities ... [more ▼]

Improvement of dairy cow fertility by means of genetic selection has become increasingly important over the last decades. Because fertility traits are difficult to measure and have low heritabilities, indicator traits are of interest to supplement the prediction of genetic merit for female fertility. This paper examines milk-based traits that could be potential predictor of fertility: changes in protein and fat composition, fat to protein ratio, urea, lactose, ketone bodies, and mid-infrared prediction of body energy traits. The pattern of genetic correlations between these traits and fertility over days in milk is likely related to the cow’s energy balance state. Furthermore, changes in milk fatty acid profile were demonstrated as good potential predictors of fertility. Finally, additional research is warranted to investigate the association over the lactation between fertility and changes in milk biomarkers, potentially predicted by mid-infrared analysis of milk. [less ▲]

in Book of abstracts of the 65th annual meeting of the European Federation of Animal Science (2014, August)

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as ... [more ▼]

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as methane emission (CH4), dry matter intake (DMI) and feed efficiency (FE); however they are available on a small scale. Researches showed that CH4 could be predicted from milk mid-infrared (MIR) spectra, allowing large-scale recording at low cost. The main objective of this study was to show, using a modelling approach, that DMI and FE could be derived from milk MIR spectra. For that, knowledge of body weight (BW) is required; however it was unknown in this study. Derived procedure was based on milk yield and composition, MIR CH4, and modelled standard animal requirements, allowing the prediction of expected BW. An external validation was conducted based on 91 actual records. 95% confidence limit for the difference ranged between -0.66 and 18.84 kg for BW, from -0.02 to 0.26 kg/day for DMI, and from -0.02 to 0.002 kg of fat corrected milk/kg DM for FE. Root mean square errors were 39.66 kg, 0.56 kg/d, and 0.03 kg/DM for the 3 studied traits. P-value for the t-test was not significant for BW and DMI. This suggests the possibility to obtain expected BW and therefore DMI from MIR spectra. Single trait animal test-day models used 1,291,850 records to assess the variability of studied traits. Significant variations were observed for the lactation stage, parity, genetics, and age. These findings were in agreement with the literature except for early lactation. This suggests in conclusion that the MIR information gave similar results for DMI and CH4 for the major part of lactation. The use of this novel method to predict expected BW offers new possibilities interesting for the development of genomic and genetic tools. [less ▲]

Genetic parameters for birth weight (BWT), weaning weight (WWT), and final weight (BW) were estimated for crossbred pigs from Piétrain boars raised in test station. Estimates of direct heritability were moderate (0.25 to 0.42), suggesting that genetic improvement of growth would be possible. Estimates of maternal heritability were 0.24 for BWT and WWT, and 0.05 for BW, indicating that the genetic influence of the dam on growth was not negligible until weaning. Genetic correlations between direct and maternal effects for BWT and WWT were moderate and unfavorable (-0.52 and -0.57 respectively). Direct genetic correlations were high and favorable between traits (0.40 to 0.75), suggesting that a high BWT is a good predictor to produce pigs with high final weight. Maternal genetic correlations between traits were low (0.01 to 0.03). Selection for higher BWT would increase final market weight but should be balanced with survival traits. [less ▲]